Thanks to the exponential growth in computing power and vast amounts of data, artificial
intelligence (AI) has witnessed remarkable developments in recent years, enabling it to be …

Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

Continual learning (CL) aims to develop techniques by which a single model adapts to an
increasing number of tasks encountered sequentially, thereby potentially leveraging …

Data selection methods, such as active learning and core-set selection, are useful tools for
machine learning on large datasets. However, they can be prohibitively expensive to apply …

Coresets are small data summaries that are sufficient for model training. They can be
maintained online, enabling efficient handling of large data streams under resource …

Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

The class of random features is one of the most popular techniques to speed up kernel
methods in large-scale problems. Related works have been recognized by the NeurIPS Test …

Leveraging the wealth of unlabeled data produced in recent years provides great potential
for improving supervised models. When the cost of acquiring labels is high, probabilistic …

We study (differentially) private federated learning (FL) of language models. The language
models in cross-device FL are relatively small, which can be trained with meaningful formal …